Natural Language Processing Specialization

Week 1: Auto-correct

Learn about autocorrect, minimum edit distance, and dynamic programming, then build your own spellchecker to correct misspelled words.

Week 2: Part-of-Speech (POS) Tagging and Hidden Markov Models

Learn about Markov chains and Hidden Markov models, then use them to create part-of-speech tags for a Wall Street Journal text corpus.

Week 3: Auto-complete and Language Models

Learn about how N-gram language models work by calculating sequence probabilities, then build your own autocomplete language model using a text corpus from Twitter.

Week 4: Word Embeddings with Neural Networks

Learn how word embeddings carry the semantic meaning of words, making them more powerful for NLP tasks. Then build your own Continuous bag-of-words model to create word embeddings from Shakespeare text.

Week 1: Neural Network for Sentiment Analysis

Learn about neural networks for deep learning, then build a sophisticated tweet classifier that places tweets into positive or negative sentiment categories using a deep neural network.

Week 2: Recurrent Neural Networks for Language Modeling

Learn about the limitations of traditional language models and see how RNNs and GRUs use sequential data for text prediction. Then build your own next-word generator using a simple RNN on Shakespeare text data.

Week 3: LSTMs and Named Entity Recognition (NER)

Learn how long short-term memory units (LSTMs) solve the vanishing gradient problem and how Named Entity Recognition systems quickly extract essential information from text. Then build your own Named Entity Recognition system using an LSTM and data from Kaggle.

Week 4: Siamese Networks

Learn about Siamese networks, a special type of neural network made of two identical networks that are eventually merged, then build your own Siamese network that identifies question duplicates in a dataset from Quora.

Week 1: Neural Machine Translation with Attention models

Discover some of the shortcomings of a traditional seq2seq model and how to solve for them by adding an attention mechanism, then build a Neural Machine Translation model with Attention that translates English sentences into German.

Week 2: Text Summarization with Transformer models

Compare RNNs and other sequential models to the more modern Transformer architecture, then create a tool that generates text summaries.

Week 3: Question-Answering

Explore transfer learning with state-of-the-art models like T5 and BERT, then build a model that can answer questions.

Week 4: Chatbots with Reformer models

Examine some unique challenges Transformer models face and their solutions, then build a chatbot using a Reformer model.